Abstract
Every metropolitan trip is punctuated by traffic signals, which have an
immediate effect on drivers, the environment, and the economy whether
the route is crowded or not. Traffic signal automation to reduce traffic
delay is a major issue all over the world. Nevertheless, the current
solutions to reduce exponentially rising traffic issues are not
completely dealing with the problem. Companies, traffic engineers and
researchers have suggested several Traffic Signal control systems. The
main function of the traffic signal management system is to coordinate
individual traffic signals to accomplish operational goals for the
entire network. The single junction-based systems are unable to reduce
the waiting time of exponentially increasing traffic load on the roads.
To deal with this, we propose collaborative signal automation on a
traffic simulator based on reinforcement learning techniques. The model
utilized a q-learning technique that depicts composing units of
addressed issues: agents, surrounding and response. The collaborative
network takes advantage of traffic flow prediction with signal
automation. Multi-junction road environments and vehicles are fed to the
network as input. The proposed system suggests optimal signal automation
to alleviate delay time and sequence length of traffic. Q-learning-based
model decreases the wait time and leads to a steady flow of vehicles
with several significances in composite traffic areas.